Visible Minority

Multi-category dot-density maps often work well when the categories cluster geographically. Visible minority data work well for this. ## Data First we grab the immigrant data via cancensus, making use of the CensusMapper API tool to select the regions and variables we need.

#devtools::install_github("mountainmath/cancensus")
library(cancensus)
library(dotdensity)
library(cancensusHelpers)
# options(cancensus.api_key='your_api_key')
regions=list(CMA="59933")
vectors=search_census_vectors("Total - Visible minority for the population in private households", "CA16","Total") %>% child_census_vectors(TRUE) %>% pull("vector")

We choose the categories and colours we want to map and define a convenience function to rename the variables and compute the qantities for the other asian countries that we don’t break out.

categories=c("South Asian", "Chinese", "Black", "Filipino", "Latin American", "Arab", "Southeast Asian", "West Asian", "Korean", "Japanese", "Visible minority, n.i.e.", "Multiple visible minorities", "Not a visible minority")
colors=c("#33a02c", "#e31a1c", "#b15928", "#1f78b4", "#fdbf6f", "#ff7f00", "#fb9a99", "#b2df8a", "#cab2d6", "#6a3d9a", "#a6cee3", "#ffff99", "#e0e0e0")
prep_data <- function(geo){
  data <- geo@data %>% replace(is.na(.), 0)
  geo@data <- data
  geo <- detail_labels(geo)
  return(geo)
}

Next we grab the data via cancensus,

data_csd=get_census(dataset = 'CA16', regions=regions,vectors=vectors,geo_format='sp',labels='short',level='CSD') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "/Users/jens/.cancensus_cache/CM_geo_6b42dc9bcf266cb7c9bfbbbf40889c49.geojson", layer: "OGRGeoJSON"
with 39 features
It has 11 fields

data_ct=get_census(dataset = 'CA16', regions=regions,vectors=vectors,geo_format='sp',labels='short',level='CT') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "/Users/jens/.cancensus_cache/CM_geo_4a5626a6774fefc22c656038b44803a8.geojson", layer: "OGRGeoJSON"
with 478 features
It has 11 fields

data_da=get_census(dataset = 'CA16', regions=regions,vectors=vectors,geo_format='sp',labels='short',level='DA') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "/Users/jens/.cancensus_cache/CM_geo_e0f185a0c335b032df17fa063ccc9238.geojson", layer: "OGRGeoJSON"
with 3450 features
It has 10 fields

data_db=get_census(dataset = 'CA16', regions=regions,geo_format='sp',labels='short',level='DB')

OGR data source with driver: GeoJSON 
Source: "/Users/jens/.cancensus_cache/CM_geo_ebd06436dd29d8db88d687516f5a8c04.geojson", layer: "OGRGeoJSON"
with 15197 features
It has 10 fields

which we then re-aggregate to make sure we don’t miss overall counts due to privacy cutoffs distribute them proportionally among the population.

data_ct@data <- dot_density.proportional_re_aggregate(data=data_ct@data,parent_data=data_csd@data,geo_match=setNames("GeoUID","CSD_UID"),categories=categories,base="Population")
data_da@data <- dot_density.proportional_re_aggregate(data=data_da@data,parent_data=data_ct@data,geo_match=setNames("GeoUID","CT_UID"),categories=categories,base="Population")
data_db@data <- dot_density.proportional_re_aggregate(data=data_db@data,parent_data=data_da@data,geo_match=setNames("GeoUID","DA_UID"),categories=categories,base="Population")

Map

All that’s left to do is to covert our re-aggregated block-level data to dots, using the dot_density.compute_dots function from the dotdensity package and feed it into the dot_density.dots_map function to add them to our basemap.

# 1 dot = 5 immigrants
scale=25
tight_x <- c(-123.28857421875,-122.96310424804688)
tight_y <- c(49.16329776043147,49.347493514019895)
wide_x <-c(-123.29,-122.6)
wide_y <- c(49.02,49.35)
dots.db <- dot_density.compute_dots(geo_data = data_db, categories = categories, scale=scale)
basemap +
  # zoom in a bit
  coord_fixed(xlim=wide_x, ylim=wide_y, ratio = 1/cos(49.2/180*pi)) +
# shade unpopulated blocks
#  geom_polygon(data=data_db[data_db$Population<=5,], 
#                        aes(long, lat, group = group), 
#                        fill = "#222222", size=0.1, 
#                        color = "#222222") +
  scale_colour_manual(values = colors) +
  labs(color = "",
                title="Visible Minority 2016",
                caption="Source: StatCan Census 2016 via cancensus & CensusMapper.ca",
                subtitle = paste0("1 dot = ",scale," people")) + 
  dot_density.dots_map(dots=dots.db,alpha=0.75,size=0.25)

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

# save image for later
ggsave('../images/minority.png',width=26,height=26)

scale=10
dots.db2 <- dot_density.compute_dots(geo_data = data_db, categories = categories, scale=scale)
basemap +
  # zoom in a bit
  coord_fixed(xlim=tight_x, ylim=tight_y, ratio = 1/cos(49.2/180*pi)) +
# shade unpopulated blocks
#  geom_polygon(data=data_db[data_db$Population<=5,], 
#                        aes(long, lat, group = group), 
#                        fill = "#222222", size=0.1, 
#                        color = "#222222") +
  scale_colour_manual(values = colors) +
  labs(color = "",
                title="Visible Minority 2016",
                caption="Source: StatCan Census 2016 via cancensus & CensusMapper.ca",
                subtitle = paste0("1 dot = ",scale," people")) + 
  dot_density.dots_map(dots=dots.db2,alpha=0.75,size=0.25)

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

# save image for later
ggsave('../images/minority_van.png',width=26,height=26)